Predictive Crime Rate Analysis System

Abstract: Crime prediction plays a crucial role in enhancing urban safety and aiding law enforcement agencies in proactive

decision-making. This study presents a predictive crime rate analysis system utilizing advanced machine learning techniques to

analyse historical crime data and forecast future crime trends. The proposed approach integrates multiple models, including

Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Decision Trees, and Random Forest, to identify patterns

and correlations across various crime types, time periods, and geographic locations. Feature engineering techniques are

employed to preprocess data, handle missing values, and extract key insights. The system provides real-time crime risk

assessments, helping law enforcement optimize resource allocation and strategic planning. Additionally, a web-based platform is

developed to enhance accessibility, allowing users to visualize crime predictions based on time, location, and crime category.

Experimental results demonstrate the effectiveness of the proposed model, achieving high accuracy in crime prediction and

forecasting. This research contributes to improving urban safety by leveraging data-driven insights for crime prevention and law

enforcement strategies.